Respirometry Trails for Summer Krill 2019
Hello World
Author: OA Lab, NWFSC Title: Respirometry Trails for Summer Krill 2019 Date: November 2020
1.) Setting Working Directory
2.) Creating the Dataframe
#*********************************
## 2.) Creating the intial Dataframe, dRESP
#*********************************
## Method 1 - 1 file for the four trials
#get all the files in the defined working directory that are in csv format
dRESP <- read.csv(file = "KRILL_Resp_alltrials.csv", stringsAsFactors = FALSE)
dim(dRESP)## [1] 3756 52write.csv(dRESP, file = "2020.11.06_presenseSENSORONLY.alltrials.data.csv", row.names = FALSE)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# dRESPanimal represents just the animal, vial, MOATs, etc.
dRESPanimal <- read.csv(file = "RespirometryTrials_all.Animal.Info.csv")
dim(dRESPanimal)## [1] 76 10write.csv(dRESPanimal, file = "2020.11.06_krillanimaldata.csv", row.names = FALSE)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#dRESPmsr represents the measurements made by the presense optical device for Dissolved Oxygen
dRESPmsr <- merge(dRESP, dRESPanimal, by="SensorName")
write.csv(dRESPmsr, file = "2020.11.06_respirometrymeasurements.csv", row.names = FALSE)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# dRESP$MOATS <- factor(dRESP$MOATS)
# Checking the names of the different levels
# levels(dRESP$MOATS)
dRESPmsr <- dRESPmsr %>% filter(!MOATS %in% c("3", "4", "5", "11")) %>%
filter(Treatment %in% c("CUR", "CHG", "TMP")) 3.) Creating dateTime objects
#*********************************
## 3.) Creating dateTime objects
#*********************************
dRESPmsr$dateTime <- ""
dRESPmsr$dateTime <- as.POSIXct(paste(dRESPmsr$Date,
dRESPmsr$Time), "%d-%B-%y %H:%M:%S", tz="UTC")
# QA check
dim(dRESPmsr)## [1] 2419 624.) Cleaning up observations
#*********************************
## 4.) Cleaning up observations
#********************************
#Removing items with faulty sensor names
dRESPmsr <- subset(dRESPmsr, SensorName != "")
dRESPmsr$SensorName <- str_trim(dRESPmsr$SensorName, side = c("both"))
dRESPmsr$SensorName <- factor(dRESPmsr$SensorName)
dim(dRESPmsr)## [1] 2419 62dRESPmsr <- subset(dRESPmsr, Value != "---")
# Confirming proper number of krill
# 76, 4x19 propoer number of animals under observation
unique(dRESPmsr$SensorName)## [1] KrilLR11 KrilLR13 KrilLR14 KrilLR15 KrilLR16 KrilLR17 KrilLR18 KrILR110
## [9] KriLR111 KriLR112 KriLR113 KriLR114 KriLR115 KriLR116 KriLR117 KriLR118
## [17] KriLR119 KriLR220 KriLR223 KriLR226 KriLR227 KriLR229 KriLR232 KriLR233
## [25] KriLR234 KriLR235 KRLr3_39 KRLr3_40 KRLr3_41 KRLr3_42 KRLr3_44 KRLr3_45
## [33] KRLr3_46 KRLr3_47 KRLr3_48 KRLr3_49 KRLr3_50 KRLr3_51 KRLr3_53 KRLr3_54
## [41] KRLr3_55 KRLr3_56 KRLr3_57 KRLr4_58 KRLr4_62 KRLr4_67 KRLr4_70 KRLr4_75
## [49] KRLr4_76
## 49 Levels: KrilLR11 KrilLR13 KrilLR14 KrilLR15 KrilLR16 KrilLR17 ... KRLr4_76UniqueSensorNames <- levels(dRESPmsr$SensorName)
write.table(UniqueSensorNames, file = "2020.11.06DRESPmsr_UniqueSensorNames.csv")
kable(UniqueSensorNames)| x |
|---|
| KrilLR11 |
| KrilLR13 |
| KrilLR14 |
| KrilLR15 |
| KrilLR16 |
| KrilLR17 |
| KrilLR18 |
| KrILR110 |
| KriLR111 |
| KriLR112 |
| KriLR113 |
| KriLR114 |
| KriLR115 |
| KriLR116 |
| KriLR117 |
| KriLR118 |
| KriLR119 |
| KriLR220 |
| KriLR223 |
| KriLR226 |
| KriLR227 |
| KriLR229 |
| KriLR232 |
| KriLR233 |
| KriLR234 |
| KriLR235 |
| KRLr3_39 |
| KRLr3_40 |
| KRLr3_41 |
| KRLr3_42 |
| KRLr3_44 |
| KRLr3_45 |
| KRLr3_46 |
| KRLr3_47 |
| KRLr3_48 |
| KRLr3_49 |
| KRLr3_50 |
| KRLr3_51 |
| KRLr3_53 |
| KRLr3_54 |
| KRLr3_55 |
| KRLr3_56 |
| KRLr3_57 |
| KRLr4_58 |
| KRLr4_62 |
| KRLr4_67 |
| KRLr4_70 |
| KRLr4_75 |
| KRLr4_76 |
#5.) Creating Trial ID & Krill ID
#*********************************
## 5.) Creating a Krill ID with Trial in name
#*********************************
## First Trial began at 2019-10-28 14:25:01
## First Trial ended at 2019-10-28 16:37:29
## Second Trial began at 2019-10-28 18:55:21
## Second Trial ended at 2019-10-28 21:16:45
## Third Trial began at 2019-10-29 14:06:21
## Third Trial ended at 2019-10-29 16:18:36
## Fourth Trial began at 2019-10-29 17:20:21
## Fourth Trial ended at 2019-10-29 19:32:01
dRESPmsr$TrialID <- ""
dRESPmsr <- dRESPmsr %>% mutate(TrialID=case_when(
## First Trial began at 2019-10-28 14:25:01
## First Trial ended at 2019-10-28 16:37:29
((Time >= as.POSIXct("2019-10-28 14:25:00", tz = "UTC"))
& (Time < as.POSIXct("2019-10-28 16:37:30", tz = "UTC"))) ~ "Trial01",
## Second Trial began at 2019-10-28 18:55:21
## Second Trial ended at 2019-10-28 21:16:45
((Time >= as.POSIXct("2019-10-28 18:55:20", tz = "UTC"))
& (Time <= as.POSIXct("2019-10-28 21:16:50", tz = "UTC"))) ~ "Trial02",
## Third Trial began at 2019-10-29 14:06:21
## Third Trial ended at 2019-10-29 16:18:36
((Time >= as.POSIXct("2019-10-29 14:06:18", tz = "UTC"))
& (Time <= as.POSIXct("2019-10-29 16:18:40", tz = "UTC"))) ~ "Trial03",
## Fourth Trial began at 2019-10-29 17:20:21
## Fourth Trial ended at 2019-10-29 19:32:01
((Time >= as.POSIXct("2019-10-29 17:20:18", tz = "UTC"))
& (Time <= as.POSIXct("2019-10-29 19:40:01", tz = "UTC"))) ~ "Trial04",
TRUE ~"other"
))
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
## new value
## dRESPmsr$KrillID
dRESPmsr$KrillID <- ""
dRESPmsr$KrillID <- paste(dRESPmsr$TrialID, "_", dRESPmsr$SensorName, sep="")
#View(dRESPmsr)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |6.) DO Corrections
#*********************************
## 6.) DO trial 3 correction applied to all rounds
#*********************************
# New values to correct the DO values for the temperature in Trial 3 in the dataframe dRESPmsr
dRESPmsr$percentDOassumpt <- ""
dRESPmsr$assumedSatDOmg <- ""
dRESPmsr$percentDO <- ""
dRESPmsr$obseveredSatDOmg <- ""
dRESPmsr$actualDOmg <- ""
dRESPmsr$percentDOassumpt <- as.numeric(dRESPmsr$percentDOassumpt)
dRESPmsr$assumedSatDOmg <- as.numeric(dRESPmsr$assumedSatDOmg)
dRESPmsr$percentDO <- as.numeric(dRESPmsr$percentDO)
dRESPmsr$obseveredSatDOmg <- as.numeric(dRESPmsr$obseveredSatDOmg)
dRESPmsr$actualDOmg <- as.numeric(dRESPmsr$actualDOmg)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#Correct Temperature
dRESPmsr$CorTemp <- 11
#Salinity Constant
dRESPmsr$SalinityConstant <- 30.3
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Numeric Corrections to DO value
dRESPmsr$Value <- as.numeric(dRESPmsr$Value)
dRESPmsr$assumedSatDOmg <- oxySol(dRESPmsr$CorTemp,
dRESPmsr$SalinityConstant)
dRESPmsr$percentDOassumpt <- dRESPmsr$Value / dRESPmsr$assumedSatDOmg
dRESPmsr$obseveredSatDOmg <- oxySol(dRESPmsr$CorTemp, dRESPmsr$SalinityConstant)
dRESPmsr$percentDO <- dRESPmsr$Value / dRESPmsr$assumedSatDOmg
dRESPmsr$actualDOmg <- dRESPmsr$percentDO * dRESPmsr$obseveredSatDOmg
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |6.a) DO table & DO correction Plot Check
#*********************************
## 6.a) DO table & DO correction Plot Check
#*********************************
# write.table(dRESPmsr, file = "2020.11.06.dRESPmeasurements", sep=";",
# row.names = TRUE)
#
# dRESPmsr <-subset(dRESPmsr, actualDOmg != "---")
# dRESPmsr$Time <- as.POSIXct(dRESPmsr$Time, format="%H:%M:%S")
ggplot(dRESPmsr, aes(x = Time, y = actualDOmg, colour = SensorName)) +
geom_point(size = 1) +
geom_smooth(method = "lm") +
facet_wrap(~TrialID) +
#scale_y_continuous(breaks=seq(250,950,by=50)) +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Filtering Broken MOATs
Plotting Animals on 80 Dissolved Oxygen
Trial1percentDO <- filter(dRESPmsr, TrialID == "Trial01")
ggplot(Trial1percentDO, aes(x = Time, y = percentDO, colour = SensorName)) +
geom_point(size = 1) +
geom_smooth(method = "lm") +
#scale_y_continuous(breaks=seq(250,950,by=50)) +
ggtitle("Trial01 Percent DO") +
facet_wrap(~ Treatment) +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Trial2percentDO <- filter(dRESPmsr, TrialID == "Trial02")
ggplot(Trial2percentDO, aes(x = Time, y = percentDO, colour = SensorName)) +
geom_point(size = 1) +
geom_smooth(method = "lm") +
#scale_y_continuous(breaks=seq(250,950,by=50)) +
ggtitle("Trial02 Percent DO") +
facet_wrap(~ Treatment) +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Trial3percentDO <- filter(dRESPmsr, TrialID == "Trial03")
ggplot(Trial3percentDO, aes(x = Time, y = percentDO, colour = SensorName)) +
geom_point(size = 1) +
geom_smooth(method = "lm") +
#scale_y_continuous(breaks=seq(250,950,by=50)) +
ggtitle("Trial03 Percent DO") +
facet_wrap(~ Treatment) +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Trial4percentDO <- filter(dRESPmsr, TrialID == "Trial04")
ggplot(Trial4percentDO, aes(x = Time, y = percentDO, colour = SensorName)) +
geom_point(size = 1) +
geom_smooth(method = "lm") +
#scale_y_continuous(breaks=seq(250,950,by=50)) +
ggtitle("Trial04 Percent DO") +
facet_wrap(~ Treatment) +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
apply the blanks within trial don’t comare blanks across trials exhibit the blanks are showing a reduction in oxygen any time that there is “evidence” of oxygen production place to zero
background bacteria breathing our water investigation
7.) Analysis, Respirometry - Creating the Slope Function
#*********************************
## 7.) Analysis, Respirometry - Creating the Slope Function
#*********************************
vialVol <- 28.06 #ml
oxygen <- vialVol * dRESPmsr$actualDOmg
dRESPmsr$oxygen <- oxygen
#nmol/vial; conversion for L to ml and umol to nmol cancels
#slope funcion of 2 vectors
slope <- function(y,x){
return(lm(y~x, na.action = na.omit)$coefficients[2])
}
dRESPmsr$delta_t <- as.numeric(dRESPmsr$delta_t)
cSlopes <- by(dRESPmsr, dRESPmsr$KrillID, function(x){ slope(x$oxygen, x$delta_t)})
#creating a data frame instead of a list
ds <- as.data.frame(sapply(cSlopes, I))
#having row names be a variable in a column to be able to pull it out for later merging
ds$TrialID<- row.names(ds)8.) Creating the ds dataframe
#*********************************
## 8.) Creating the ds dataframe
#*********************************
#creating a data frame instead of a list
ds <- as.data.frame(sapply(cSlopes, I))
#having row names be a variable in a column to be able to pull it out for later merging
ds$TrialID<- row.names(ds)
dim(ds)## [1] 49 2#add column to dslopes thats KrillID
#View(dref)
ds$KrillID <- row.names(ds)
#View(dslopes)
dtotal <- merge(dRESPmsr, ds, by = "KrillID")
#View(dtotal)
unique(dtotal$KrillID)## [1] "Trial01_KrilLR11" "Trial01_KrilLR13" "Trial01_KrilLR14" "Trial01_KrilLR15"
## [5] "Trial01_KrilLR16" "Trial01_KrilLR17" "Trial01_KrilLR18" "Trial01_KrILR110"
## [9] "Trial01_KriLR111" "Trial01_KriLR112" "Trial01_KriLR113" "Trial01_KriLR114"
## [13] "Trial01_KriLR115" "Trial01_KriLR116" "Trial01_KriLR117" "Trial01_KriLR118"
## [17] "Trial01_KriLR119" "Trial02_KriLR220" "Trial02_KriLR223" "Trial02_KriLR226"
## [21] "Trial02_KriLR227" "Trial02_KriLR229" "Trial02_KriLR232" "Trial02_KriLR233"
## [25] "Trial02_KriLR234" "Trial02_KriLR235" "Trial03_KRLr3_39" "Trial03_KRLr3_40"
## [29] "Trial03_KRLr3_41" "Trial03_KRLr3_42" "Trial03_KRLr3_44" "Trial03_KRLr3_45"
## [33] "Trial03_KRLr3_46" "Trial03_KRLr3_47" "Trial03_KRLr3_48" "Trial03_KRLr3_49"
## [37] "Trial03_KRLr3_50" "Trial03_KRLr3_51" "Trial03_KRLr3_53" "Trial03_KRLr3_54"
## [41] "Trial03_KRLr3_55" "Trial03_KRLr3_56" "Trial03_KRLr3_57" "Trial04_KRLr4_58"
## [45] "Trial04_KRLr4_62" "Trial04_KRLr4_67" "Trial04_KRLr4_70" "Trial04_KRLr4_75"
## [49] "Trial04_KRLr4_76"9.) Creating Slope Functions & Linear Models
#*********************************
## 9.) Creating the ds dataframe
#*********************************
# #get slopes and r^2 values from dtotal - just adjusted code from data analysis to fit into these df's
dtotal$KrillID <- factor(dtotal$KrillID)
nlevels(dtotal$KrillID)## [1] 49## [1] Trial01_KrilLR11 Trial01_KrilLR13 Trial01_KrilLR14 Trial01_KrilLR15
## [5] Trial01_KrilLR16 Trial01_KrilLR17 Trial01_KrilLR18 Trial01_KrILR110
## [9] Trial01_KriLR111 Trial01_KriLR112 Trial01_KriLR113 Trial01_KriLR114
## [13] Trial01_KriLR115 Trial01_KriLR116 Trial01_KriLR117 Trial01_KriLR118
## [17] Trial01_KriLR119 Trial02_KriLR220 Trial02_KriLR223 Trial02_KriLR226
## [21] Trial02_KriLR227 Trial02_KriLR229 Trial02_KriLR232 Trial02_KriLR233
## [25] Trial02_KriLR234 Trial02_KriLR235 Trial03_KRLr3_39 Trial03_KRLr3_40
## [29] Trial03_KRLr3_41 Trial03_KRLr3_42 Trial03_KRLr3_44 Trial03_KRLr3_45
## [33] Trial03_KRLr3_46 Trial03_KRLr3_47 Trial03_KRLr3_48 Trial03_KRLr3_49
## [37] Trial03_KRLr3_50 Trial03_KRLr3_51 Trial03_KRLr3_53 Trial03_KRLr3_54
## [41] Trial03_KRLr3_55 Trial03_KRLr3_56 Trial03_KRLr3_57 Trial04_KRLr4_58
## [45] Trial04_KRLr4_62 Trial04_KRLr4_67 Trial04_KRLr4_70 Trial04_KRLr4_75
## [49] Trial04_KRLr4_76
## 49 Levels: Trial01_KrilLR11 Trial01_KrilLR13 ... Trial04_KRLr4_76#how best to run the lmlist over
# group and then run a function tidyverse
# group by krill ID and then run the function
levels(dtotal$KrillID)## [1] "Trial01_KrilLR11" "Trial01_KrilLR13" "Trial01_KrilLR14" "Trial01_KrilLR15"
## [5] "Trial01_KrilLR16" "Trial01_KrilLR17" "Trial01_KrilLR18" "Trial01_KrILR110"
## [9] "Trial01_KriLR111" "Trial01_KriLR112" "Trial01_KriLR113" "Trial01_KriLR114"
## [13] "Trial01_KriLR115" "Trial01_KriLR116" "Trial01_KriLR117" "Trial01_KriLR118"
## [17] "Trial01_KriLR119" "Trial02_KriLR220" "Trial02_KriLR223" "Trial02_KriLR226"
## [21] "Trial02_KriLR227" "Trial02_KriLR229" "Trial02_KriLR232" "Trial02_KriLR233"
## [25] "Trial02_KriLR234" "Trial02_KriLR235" "Trial03_KRLr3_39" "Trial03_KRLr3_40"
## [29] "Trial03_KRLr3_41" "Trial03_KRLr3_42" "Trial03_KRLr3_44" "Trial03_KRLr3_45"
## [33] "Trial03_KRLr3_46" "Trial03_KRLr3_47" "Trial03_KRLr3_48" "Trial03_KRLr3_49"
## [37] "Trial03_KRLr3_50" "Trial03_KRLr3_51" "Trial03_KRLr3_53" "Trial03_KRLr3_54"
## [41] "Trial03_KRLr3_55" "Trial03_KRLr3_56" "Trial03_KRLr3_57" "Trial04_KRLr4_58"
## [45] "Trial04_KRLr4_62" "Trial04_KRLr4_67" "Trial04_KRLr4_70" "Trial04_KRLr4_75"
## [49] "Trial04_KRLr4_76"GRP_dtotal <- dtotal %>% group_by(KrillID)
dtotal <- GRP_dtotal
info <- lmList(oxygen ~ delta_t|KrillID, dtotal,na.action=na.omit)
#view(dtotal[!dtotal$KrillID %in% names(info), ])9.a Missing Slope Value
MissingINFOobservations <- (dtotal[!dtotal$KrillID %in% names(info), ])
dtotalVSmissinfo <- summary(comparedf(dtotal, MissingINFOobservations))
kable(MissingINFOobservations)| KrillID | SensorName | Date | Time | User | SensorID | delta_t | Unit | Value | Unit.1 | Mode | Phase | Unit.2 | Amplitude | Unit.3 | Temp | Unit.4 | patm | Unit.5 | Salinity | Unit.6 | Error | Cal0 | Unit.7 | T0 | Unit.8 | O2.2nd | Unit.9 | Cal2nd | Unit.10 | T2nd | Unit.11 | CalPressure | Unit.12 | f1 | dPhi1 | dkSv1 | dPhi2 | dkSv2 | m | Cal.Mode | SignalLEDCurrent | ReferenceLEDCurrent | ReferenceAmplitude | DeviceSerial | FwVersion | SensorType | BatchID | Calibration.Date | Sensor.lot | PreSens.calibrated.Sensor | Battery.Voltage | Unit.13 | Sequence | FileName | MOATS | Treatment | LoadingLocation | GlassVialNumber | Species | TelsonLength..mm. | Size | dateTime | TrialID.x | percentDOassumpt | assumedSatDOmg | percentDO | obseveredSatDOmg | actualDOmg | CorTemp | SalinityConstant | oxygen | sapply(cSlopes, I) | TrialID.y |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9.b Exploring 3 Missing Observations
write.csv(MissingINFOobservations, file = "2020.11.16_MissingINFOobservations.csv", row.names = FALSE)
#Summary function commented out due to errors below
summary(comparedf(dtotal, MissingINFOobservations, by = "delta_t"))##
##
## Table: Summary of data.frames
##
## version arg ncol nrow
## -------- ------------------------ ----- -----
## x dtotal 74 2382
## y MissingINFOobservations 74 0
##
##
##
## Table: Summary of overall comparison
##
## statistic value
## ------------------------------------------------------------ ------
## Number of by-variables 1
## Number of non-by variables in common 73
## Number of variables compared 73
## Number of variables in x but not y 0
## Number of variables in y but not x 0
## Number of variables compared with some values unequal 0
## Number of variables compared with all values equal 73
## Number of observations in common 0
## Number of observations in x but not y 2382
## Number of observations in y but not x 0
## Number of observations with some compared variables unequal 0
## Number of observations with all compared variables equal 0
## Number of values unequal 0
##
##
##
## Table: Variables not shared
##
## | |
## |:-----------------------|
## |No variables not shared |
##
##
##
## Table: Other variables not compared
##
## | |
## |:-------------------------------|
## |No other variables not compared |
##
##
##
## Table: Observations not shared
##
## version delta_t observation
## -------- -------- ------------
## x 0.00 1
## x 0.00 837
## x 0.00 1297
## x 0.00 2125
## x 0.02 2
## x 0.02 838
## x 0.02 1298
## x 0.02 2126
## x 0.03 3
## x 0.03 839
## x 0.03 1299
## x 0.03 2127
## x 0.05 840
## x 0.05 1300
## x 0.05 2128
## x 0.17 1385
## x 0.18 1355
## x 0.20 1351
## x 0.22 1348
## x 0.33 1425
## x 0.35 1400
## x 0.37 1420
## x 0.38 1421
## x 0.52 1481
## x 0.53 1461
## x 0.55 896
## x 0.55 1466
## x 0.57 897
## x 0.57 1462
## x 0.58 907
## x 0.60 908
## x 0.62 921
## x 0.63 922
## x 0.63 2196
## x 0.65 2184
## x 0.67 2181
## x 0.68 2182
## x 0.83 1512
## x 0.85 1517
## x 0.87 4
## x 0.87 1491
## x 0.88 5
## x 0.88 1483
## x 0.90 6
## x 0.90 1494
## x 0.92 7
## x 1.02 1581
## x 1.03 1580
## x 1.05 1550
## x 1.07 1551
## x 1.08 1548
## x 1.20 1630
## x 1.22 74
## x 1.22 1627
## x 1.23 75
## x 1.23 1619
## x 1.25 62
## x 1.25 1588
## x 1.27 63
## x 1.27 1589
## x 1.40 1651
## x 1.42 1652
## x 1.43 1669
## x 1.45 1633
## x 1.47 104
## x 1.47 1638
## x 1.48 105
## x 1.50 119
## x 1.55 2232
## x 1.57 2233
## x 1.58 1729
## x 1.58 2234
## x 1.60 1732
## x 1.62 1731
## x 1.63 1698
## x 1.68 145
## x 1.70 147
## x 1.72 143
## x 1.75 1769
## x 1.77 1774
## x 1.78 1775
## x 1.80 937
## x 1.80 1776
## x 1.82 938
## x 1.83 932
## x 1.85 953
## x 1.87 197
## x 1.87 954
## x 1.88 187
## x 1.88 955
## x 1.90 188
## x 1.90 969
## x 1.92 970
## x 1.93 1785
## x 1.95 1790
## x 1.97 1825
## x 1.98 1828
## x 2.07 971
## x 2.07 2235
## x 2.07 2236
## x 2.08 241
## x 2.08 968
## x 2.08 2237
## x 2.10 242
## x 2.10 973
## x 2.12 250
## x 2.12 974
## x 2.13 947
## x 2.13 1870
## x 2.15 948
## x 2.15 1874
## x 2.17 962
## x 2.17 1875
## x 2.18 963
## x 2.18 1876
## x 2.25 318
## x 2.27 320
## x 2.28 319
## x 2.30 306
## x 2.52 1019
## x 2.52 1878
## x 2.53 1882
## x 2.55 1009
## x 2.55 1877
## x 2.57 1010
## x 2.57 1897
## x 2.58 997
## x 2.58 1913
## x 2.60 998
## x 2.62 996
## x 2.65 348
## x 2.65 2283
## x 2.67 358
## x 2.67 2270
## x 2.68 365
## x 2.68 2287
## x 2.70 370
## x 2.70 1970
## x 2.70 2288
## x 2.72 1973
## x 2.73 1969
## x 2.75 1974
## x 2.87 1995
## x 2.88 1996
## x 2.90 1997
## x 2.92 404
## x 2.92 1998
## x 2.93 416
## x 2.95 413
## x 2.97 425
## x 3.03 2023
## x 3.05 2025
## x 3.07 2029
## x 3.08 2026
## x 3.15 471
## x 3.17 470
## x 3.18 465
## x 3.20 2072
## x 3.22 2084
## x 3.23 2091
## x 3.25 2107
## x 3.27 2113
## x 3.32 1080
## x 3.33 528
## x 3.33 1077
## x 3.35 519
## x 3.35 1073
## x 3.37 506
## x 3.37 1054
## x 3.38 505
## x 3.38 1053
## x 3.45 2335
## x 3.47 2327
## x 3.48 2328
## x 3.50 2329
## x 3.55 571
## x 3.57 549
## x 3.58 550
## x 3.65 2369
## x 3.67 2370
## x 3.68 2371
## x 3.72 615
## x 3.72 625
## x 3.73 622
## x 3.75 592
## x 3.77 597
## x 3.90 650
## x 3.92 651
## x 3.93 665
## x 3.95 631
## x 3.98 1114
## x 4.00 1107
## x 4.02 1103
## x 4.03 1129
## x 4.05 1108
## x 4.07 1116
## x 4.10 720
## x 4.12 694
## x 4.13 686
## x 4.15 696
## x 4.27 764
## x 4.28 765
## x 4.30 771
## x 4.32 767
## x 4.40 1172
## x 4.42 1151
## x 4.43 823
## x 4.43 1189
## x 4.45 824
## x 4.45 1177
## x 4.47 825
## x 4.47 1152
## x 4.48 826
## x 4.48 1154
## x 4.50 827
## x 4.83 1219
## x 4.85 1207
## x 4.87 1202
## x 4.88 1196
## x 4.90 1197
## x 5.03 1245
## x 5.05 1240
## x 5.07 1239
## x 5.08 1242
## x 5.10 1241
## x 5.27 1246
## x 5.28 1256
## x 5.30 1248
## x 8.15 2142
## x 8.17 2156
## x 8.18 2157
## x 8.20 2158
## x 8.22 2146
## x 8.88 1328
## x 8.90 1325
## x 8.92 1320
## x 8.93 1302
## x 8.93 2176
## x 8.95 1303
## x 8.95 2183
## x 8.97 2203
## x 8.98 2205
## x 9.00 2192
## x 9.05 25
## x 9.07 26
## x 9.07 1361
## x 9.08 36
## x 9.08 1356
## x 9.10 38
## x 9.10 1346
## x 9.12 1358
## x 9.23 1408
## x 9.25 1431
## x 9.27 1428
## x 9.28 1429
## x 9.30 1430
## x 9.38 71
## x 9.40 85
## x 9.42 72
## x 9.42 1465
## x 9.43 88
## x 9.43 1440
## x 9.45 91
## x 9.45 1441
## x 9.47 1446
## x 9.48 1447
## x 9.57 124
## x 9.58 129
## x 9.60 126
## x 9.62 127
## x 9.63 136
## x 9.77 168
## x 9.78 179
## x 9.78 1514
## x 9.80 175
## x 9.80 1519
## x 9.82 162
## x 9.82 1520
## x 9.83 150
## x 9.83 1521
## x 9.85 1518
## x 9.97 1546
## x 9.98 1536
## x 10.00 1533
## x 10.02 1534
## x 10.02 2225
## x 10.03 1535
## x 10.03 2213
## x 10.05 2223
## x 10.07 2224
## x 10.08 216
## x 10.08 2240
## x 10.10 221
## x 10.12 209
## x 10.13 204
## x 10.15 201
## x 10.15 1592
## x 10.17 1603
## x 10.18 1611
## x 10.20 1631
## x 10.22 1628
## x 10.27 251
## x 10.28 247
## x 10.30 262
## x 10.32 245
## x 10.33 266
## x 10.35 1668
## x 10.37 1654
## x 10.38 1655
## x 10.40 1656
## x 10.42 1657
## x 10.47 307
## x 10.48 294
## x 10.50 322
## x 10.52 301
## x 10.53 297
## x 10.55 1697
## x 10.57 1687
## x 10.58 1683
## x 10.60 1685
## x 10.62 1706
## x 10.73 2296
## x 10.75 2291
## x 10.77 1771
## x 10.77 2292
## x 10.78 1773
## x 10.78 2281
## x 10.80 1777
## x 10.80 2279
## x 10.82 1778
## x 10.83 1779
## x 10.90 376
## x 10.92 369
## x 10.93 374
## x 10.95 385
## x 10.95 1789
## x 10.97 1794
## x 10.98 1791
## x 11.00 1792
## x 11.02 1793
## x 11.08 428
## x 11.10 421
## x 11.12 430
## x 11.13 395
## x 11.20 1829
## x 11.22 1832
## x 11.23 1843
## x 11.25 474
## x 11.25 1845
## x 11.27 469
## x 11.27 1858
## x 11.28 466
## x 11.30 454
## x 11.43 508
## x 11.45 509
## x 11.47 496
## x 11.62 537
## x 11.63 538
## x 11.65 552
## x 11.67 562
## x 11.68 553
## x 11.70 1880
## x 11.72 1885
## x 11.73 1886
## x 11.75 1879
## x 11.77 1884
## x 11.80 588
## x 11.82 594
## x 11.83 595
## x 11.85 583
## x 11.90 1929
## x 11.92 1938
## x 11.93 1948
## x 11.95 652
## x 11.95 1958
## x 11.95 2338
## x 11.97 635
## x 11.97 2309
## x 11.98 632
## x 11.98 2303
## x 12.00 654
## x 12.00 2299
## x 12.02 645
## x 12.02 2311
## x 12.07 2018
## x 12.08 1999
## x 12.10 2004
## x 12.12 2005
## x 12.13 721
## x 12.15 726
## x 12.17 727
## x 12.17 2359
## x 12.18 715
## x 12.18 2351
## x 12.20 2342
## x 12.22 2346
## x 12.23 2343
## x 12.25 2066
## x 12.27 2058
## x 12.28 2028
## x 12.30 756
## x 12.30 2033
## x 12.32 757
## x 12.33 742
## x 12.35 743
## x 12.45 800
## x 12.45 2102
## x 12.47 828
## x 12.47 2103
## x 12.48 806
## x 12.48 2108
## x 12.50 803
## x 12.50 2101
## x 12.52 2106
## x 15.03 855
## x 15.05 856
## x 15.07 857
## x 15.08 871
## x 16.48 902
## x 16.50 903
## x 16.52 904
## x 16.53 905
## x 16.55 906
## x 18.28 939
## x 18.30 940
## x 18.32 941
## x 18.33 942
## x 18.50 1014
## x 18.52 995
## x 18.53 1000
## x 18.55 1006
## x 18.57 1003
## x 18.58 1008
## x 19.08 1083
## x 19.10 1084
## x 19.12 1070
## x 19.13 1062
## x 19.15 1059
## x 19.75 1106
## x 19.77 1111
## x 19.78 1135
## x 19.80 1143
## x 19.82 1137
## x 19.95 1156
## x 19.97 1148
## x 19.98 1147
## x 20.00 1150
## x 20.02 1157
## x 20.17 1200
## x 20.18 1212
## x 20.20 1206
## x 20.22 1198
## x 20.35 1247
## x 20.37 1259
## x 20.38 1269
## x 20.40 1257
## x 23.12 2163
## x 23.13 2132
## x 23.15 2133
## x 23.17 2160
## x 23.18 2151
## x 23.73 1329
## x 23.75 1326
## x 23.77 1331
## x 23.78 1308
## x 23.80 1309
## x 23.92 1372
## x 23.93 1368
## x 23.95 1364
## x 23.97 1349
## x 23.98 1350
## x 24.12 1401
## x 24.13 1396
## x 24.15 1411
## x 24.17 29
## x 24.17 1423
## x 24.18 30
## x 24.18 1433
## x 24.20 31
## x 24.30 1475
## x 24.32 1471
## x 24.33 1444
## x 24.35 1445
## x 24.37 1450
## x 24.48 73
## x 24.48 2208
## x 24.50 59
## x 24.50 2194
## x 24.52 60
## x 24.52 2195
## x 24.53 55
## x 24.53 2174
## x 24.55 2189
## x 24.63 116
## x 24.65 102
## x 24.67 103
## x 24.68 94
## x 24.68 1484
## x 24.70 1504
## x 24.72 1490
## x 24.73 1507
## x 24.75 1524
## x 24.78 155
## x 24.80 169
## x 24.82 158
## x 24.83 141
## x 24.85 139
## x 24.88 1557
## x 24.90 1562
## x 24.92 1539
## x 24.93 1540
## x 24.95 1541
## x 25.08 1602
## x 25.10 1599
## x 25.12 198
## x 25.12 1583
## x 25.13 195
## x 25.13 1584
## x 25.15 190
## x 25.15 1593
## x 25.17 185
## x 25.18 186
## x 25.28 1679
## x 25.30 1676
## x 25.32 243
## x 25.32 1661
## x 25.33 240
## x 25.33 1662
## x 25.35 253
## x 25.35 1663
## x 25.37 255
## x 25.38 252
## x 25.47 1700
## x 25.48 1705
## x 25.50 1690
## x 25.52 295
## x 25.52 1691
## x 25.53 309
## x 25.53 1688
## x 25.55 293
## x 25.57 290
## x 25.57 2222
## x 25.58 315
## x 25.58 2248
## x 25.60 2238
## x 25.62 2251
## x 25.63 2217
## x 25.70 1741
## x 25.72 1746
## x 25.73 1743
## x 25.75 1744
## x 25.77 1780
## x 25.90 1815
## x 25.92 1820
## x 25.93 350
## x 25.93 1801
## x 25.95 381
## x 25.95 1798
## x 25.97 380
## x 25.97 1799
## x 25.98 373
## x 26.00 356
## x 26.10 1852
## x 26.12 409
## x 26.12 1849
## x 26.13 410
## x 26.13 1831
## x 26.15 411
## x 26.15 1835
## x 26.17 412
## x 26.17 1840
## x 26.23 2262
## x 26.25 2276
## x 26.27 2264
## x 26.28 2259
## x 26.30 447
## x 26.30 2256
## x 26.32 457
## x 26.32 2257
## x 26.33 449
## x 26.35 446
## x 26.48 481
## x 26.50 483
## x 26.52 487
## x 26.53 485
## x 26.55 499
## x 26.57 1910
## x 26.58 1911
## x 26.60 1903
## x 26.62 1889
## x 26.63 1898
## x 26.68 531
## x 26.70 544
## x 26.72 532
## x 26.73 542
## x 26.75 534
## x 26.77 1947
## x 26.78 1944
## x 26.80 1940
## x 26.82 1927
## x 26.83 1935
## x 26.87 582
## x 26.88 579
## x 26.90 578
## x 26.92 590
## x 26.93 600
## x 26.95 2009
## x 26.97 2020
## x 26.98 2015
## x 27.00 2011
## x 27.02 2013
## x 27.08 642
## x 27.10 657
## x 27.12 658
## x 27.13 646
## x 27.13 2050
## x 27.15 656
## x 27.15 2047
## x 27.17 2048
## x 27.18 2044
## x 27.20 2038
## x 27.30 701
## x 27.30 2304
## x 27.32 688
## x 27.32 2302
## x 27.33 690
## x 27.33 2316
## x 27.35 693
## x 27.35 2317
## x 27.37 707
## x 27.38 2087
## x 27.40 2092
## x 27.42 2085
## x 27.43 2094
## x 27.45 2115
## x 27.47 2116
## x 27.48 2347
## x 27.50 752
## x 27.50 2372
## x 27.52 753
## x 27.52 2373
## x 27.53 773
## x 27.53 2374
## x 27.55 778
## x 27.55 2375
## x 27.57 779
## x 27.70 807
## x 27.72 808
## x 27.73 809
## x 27.75 810
## x 27.77 802
## x 29.63 877
## x 29.65 878
## x 29.67 865
## x 30.33 913
## x 30.35 900
## x 30.37 892
## x 30.38 885
## x 30.40 894
## x 31.15 992
## x 31.17 975
## x 31.18 950
## x 31.20 949
## x 31.33 1040
## x 31.35 1051
## x 31.37 1030
## x 31.38 1002
## x 31.40 999
## x 31.42 1004
## x 31.43 1001
## x 31.78 1095
## x 31.80 1088
## x 31.82 1078
## x 31.83 1066
## x 31.85 1067
## x 32.60 1142
## x 32.62 1140
## x 32.63 1141
## x 32.65 1120
## x 32.80 1160
## x 32.82 1165
## x 32.83 1162
## x 32.85 1186
## x 33.03 1222
## x 33.05 1214
## x 33.07 1233
## x 33.47 1258
## x 33.48 1272
## x 33.50 1273
## x 33.72 1274
## x 33.73 1275
## x 33.75 1276
## x 33.77 1277
## x 33.78 1243
## x 33.80 1251
## x 33.82 1244
## x 33.83 1249
## x 33.85 1282
## x 33.87 1283
## x 37.83 2147
## x 37.85 2148
## x 37.87 2149
## x 37.88 2150
## x 38.75 1316
## x 38.77 1317
## x 38.78 1318
## x 38.80 1310
## x 38.80 2173
## x 38.82 1315
## x 38.82 2193
## x 38.83 2185
## x 38.85 2186
## x 38.87 2166
## x 38.92 1362
## x 38.93 1354
## x 38.95 1347
## x 38.97 1344
## x 38.98 1352
## x 39.10 1391
## x 39.12 1390
## x 39.13 1405
## x 39.15 1434
## x 39.17 11
## x 39.18 12
## x 39.20 13
## x 39.22 14
## x 39.23 15
## x 39.30 1457
## x 39.32 1453
## x 39.33 1454
## x 39.35 1459
## x 39.57 52
## x 39.58 53
## x 39.60 67
## x 39.62 68
## x 39.63 56
## x 39.67 1486
## x 39.68 1489
## x 39.70 1525
## x 39.72 1527
## x 39.75 100
## x 39.77 93
## x 39.78 113
## x 39.80 114
## x 39.82 97
## x 39.83 1545
## x 39.83 1560
## x 39.85 1542
## x 39.87 1543
## x 39.88 1544
## x 39.93 156
## x 39.95 157
## x 39.97 146
## x 39.98 160
## x 40.00 148
## x 40.02 1618
## x 40.03 2244
## x 40.05 1591
## x 40.05 2245
## x 40.07 1585
## x 40.07 2253
## x 40.08 1586
## x 40.08 2247
## x 40.10 1594
## x 40.12 200
## x 40.13 192
## x 40.15 189
## x 40.17 210
## x 40.18 207
## x 40.27 1667
## x 40.28 1664
## x 40.30 1665
## x 40.32 1666
## x 40.33 248
## x 40.33 1671
## x 40.35 263
## x 40.37 281
## x 40.38 283
## x 40.40 271
## x 40.45 1711
## x 40.47 1693
## x 40.48 1694
## x 40.50 1695
## x 40.52 1692
## x 40.57 333
## x 40.58 312
## x 40.60 313
## x 40.62 327
## x 40.63 328
## x 40.65 1753
## x 40.65 2271
## x 40.67 1742
## x 40.67 2272
## x 40.68 1735
## x 40.68 2260
## x 40.70 1733
## x 40.70 2274
## x 40.72 1749
## x 40.72 2297
## x 40.83 1787
## x 40.85 1781
## x 40.87 1805
## x 40.88 1806
## x 40.90 1807
## x 41.02 362
## x 41.03 363
## x 41.03 1842
## x 41.05 364
## x 41.05 1833
## x 41.07 341
## x 41.07 1834
## x 41.08 352
## x 41.08 1839
## x 41.10 1844
## x 41.22 405
## x 41.23 393
## x 41.25 387
## x 41.27 392
## x 41.40 443
## x 41.42 435
## x 41.43 436
## x 41.45 437
## x 41.47 434
## x 41.53 1899
## x 41.55 1891
## x 41.57 1896
## x 41.58 1893
## x 41.60 1902
## x 41.63 484
## x 41.65 486
## x 41.67 491
## x 41.68 488
## x 41.70 513
## x 41.73 1950
## x 41.75 1932
## x 41.77 1937
## x 41.77 2333
## x 41.78 1930
## x 41.78 2330
## x 41.80 1939
## x 41.80 2331
## x 41.82 2336
## x 41.83 541
## x 41.83 2322
## x 41.85 555
## x 41.87 556
## x 41.88 543
## x 41.90 560
## x 41.93 1977
## x 41.95 1976
## x 41.97 2006
## x 41.98 2012
## x 41.98 2377
## x 42.00 2017
## x 42.00 2378
## x 42.02 2379
## x 42.03 2356
## x 42.05 2353
## x 42.07 609
## x 42.08 606
## x 42.10 598
## x 42.12 593
## x 42.13 613
## x 42.13 2060
## x 42.15 2049
## x 42.17 2036
## x 42.18 2041
## x 42.20 2042
## x 42.32 664
## x 42.33 671
## x 42.33 2105
## x 42.35 677
## x 42.35 2090
## x 42.37 672
## x 42.37 2074
## x 42.38 674
## x 42.38 2082
## x 42.40 2079
## x 42.53 718
## x 42.55 732
## x 42.57 708
## x 42.58 695
## x 42.60 709
## x 42.75 723
## x 42.77 725
## x 42.78 730
## x 42.80 731
## x 42.82 733
## x 42.83 738
## x 43.13 768
## x 43.15 769
## x 43.17 770
## x 43.18 749
## x 43.20 759
## x 43.35 794
## x 43.37 788
## x 43.38 792
## x 43.40 793
## x 43.42 817
## x 43.43 791
## x 46.35 858
## x 46.37 847
## x 46.38 880
## x 46.40 876
## x 46.42 881
## x 46.43 875
## x 46.45 867
## x 46.47 866
## x 47.47 893
## x 47.48 917
## x 47.50 918
## x 47.52 927
## x 47.53 924
## x 47.55 920
## x 48.27 984
## x 48.27 993
## x 48.28 989
## x 48.30 979
## x 48.32 976
## x 48.53 1049
## x 48.55 1044
## x 48.57 1047
## x 48.58 1026
## x 48.60 1018
## x 49.00 1096
## x 49.02 1101
## x 49.03 1100
## x 49.05 1098
## x 49.07 1089
## x 49.92 1113
## x 49.93 1145
## x 49.95 1144
## x 49.97 1138
## x 49.98 1133
## x 50.15 1173
## x 50.17 1174
## x 50.18 1188
## x 50.20 1190
## x 50.22 1194
## x 50.37 1226
## x 50.38 1227
## x 50.40 1238
## x 50.42 1237
## x 50.43 1234
## x 50.57 1260
## x 50.58 1261
## x 50.60 1262
## x 50.62 1290
## x 50.63 1264
## x 52.98 2153
## x 53.00 2154
## x 53.02 2155
## x 53.03 2164
## x 53.05 2129
## x 53.07 2130
## x 53.20 2131
## x 53.72 1324
## x 53.73 1341
## x 53.75 1338
## x 53.77 1330
## x 53.78 1312
## x 53.88 1387
## x 53.90 1389
## x 53.92 1370
## x 53.93 1366
## x 53.95 1357
## x 54.12 19
## x 54.12 1426
## x 54.13 20
## x 54.13 1418
## x 54.15 34
## x 54.15 1410
## x 54.17 35
## x 54.17 1427
## x 54.18 32
## x 54.18 1422
## x 54.25 2175
## x 54.27 2167
## x 54.28 2180
## x 54.30 2177
## x 54.32 2169
## x 54.38 1476
## x 54.40 1474
## x 54.42 1455
## x 54.43 1456
## x 54.45 1443
## x 54.47 78
## x 54.48 89
## x 54.50 86
## x 54.52 90
## x 54.53 83
## x 54.65 110
## x 54.67 111
## x 54.68 112
## x 54.70 122
## x 54.72 132
## x 54.73 1509
## x 54.75 1515
## x 54.77 1516
## x 54.78 1495
## x 54.80 1528
## x 54.82 176
## x 54.83 184
## x 54.85 177
## x 54.87 182
## x 54.88 181
## x 54.90 1578
## x 54.92 1556
## x 54.93 1555
## x 54.95 1553
## x 54.97 1554
## x 54.98 1532
## x 55.02 236
## x 55.02 237
## x 55.03 233
## x 55.05 239
## x 55.07 238
## x 55.12 1614
## x 55.13 1609
## x 55.15 1598
## x 55.17 1595
## x 55.18 257
## x 55.18 1582
## x 55.20 258
## x 55.22 259
## x 55.23 273
## x 55.25 261
## x 55.25 2241
## x 55.27 2242
## x 55.28 2229
## x 55.30 1670
## x 55.30 2230
## x 55.32 1674
## x 55.33 1675
## x 55.35 1672
## x 55.37 1653
## x 55.42 305
## x 55.43 302
## x 55.45 303
## x 55.47 334
## x 55.48 308
## x 55.50 1708
## x 55.52 1699
## x 55.53 1696
## x 55.55 1701
## x 55.57 1686
## x 55.68 1739
## x 55.70 1736
## x 55.72 1737
## x 55.73 1756
## x 55.78 347
## x 55.80 371
## x 55.82 372
## x 55.83 382
## x 55.85 383
## x 55.87 1812
## x 55.88 1809
## x 55.90 1810
## x 55.92 1811
## x 55.97 2265
## x 55.98 401
## x 55.98 2266
## x 56.00 426
## x 56.00 2267
## x 56.02 429
## x 56.02 2255
## x 56.03 422
## x 56.05 427
## x 56.07 1869
## x 56.08 1855
## x 56.10 1838
## x 56.12 1847
## x 56.13 1848
## x 56.20 476
## x 56.22 473
## x 56.23 480
## x 56.25 475
## x 56.27 453
## x 56.40 524
## x 56.42 529
## x 56.43 510
## x 56.45 493
## x 56.47 494
## x 56.52 1883
## x 56.53 1908
## x 56.55 1900
## x 56.57 1901
## x 56.58 1906
## x 56.62 577
## x 56.63 575
## x 56.65 540
## x 56.67 535
## x 56.68 530
## x 56.70 1968
## x 56.72 1955
## x 56.73 1941
## x 56.75 1934
## x 56.77 1943
## x 56.82 605
## x 56.83 596
## x 56.85 587
## x 56.87 580
## x 56.88 581
## x 56.90 1989
## x 56.92 1984
## x 56.93 1982
## x 56.95 1979
## x 56.97 1985
## x 56.97 2319
## x 56.98 2320
## x 57.00 2321
## x 57.02 666
## x 57.02 2301
## x 57.03 636
## x 57.03 2340
## x 57.05 648
## x 57.07 630
## x 57.08 629
## x 57.10 2035
## x 57.12 2054
## x 57.13 2045
## x 57.15 2030
## x 57.17 2024
## x 57.22 689
## x 57.23 685
## x 57.25 680
## x 57.25 2365
## x 57.27 679
## x 57.27 2366
## x 57.28 698
## x 57.28 2367
## x 57.30 2117
## x 57.30 2368
## x 57.32 2095
## x 57.32 2360
## x 57.33 2080
## x 57.35 2075
## x 57.37 2073
## x 57.38 2081
## x 57.42 755
## x 57.43 750
## x 57.45 747
## x 57.47 758
## x 57.48 745
## x 57.63 829
## x 57.65 830
## x 57.67 786
## x 57.68 804
## x 57.70 805
## x 60.78 868
## x 60.80 869
## x 60.82 870
## x 60.83 879
## x 60.85 846
## x 61.53 928
## x 61.55 898
## x 61.57 899
## x 61.58 912
## x 61.60 901
## x 62.40 931
## x 62.42 956
## x 62.43 957
## x 62.45 958
## x 62.47 959
## x 62.48 960
## x 63.25 961
## x 63.27 972
## x 63.28 977
## x 63.30 978
## x 63.32 966
## x 63.33 967
## x 64.25 1041
## x 64.27 1052
## x 64.28 1022
## x 64.30 1023
## x 64.32 1016
## x 64.33 1017
## x 64.90 1063
## x 64.92 1069
## x 64.93 1061
## x 64.95 1058
## x 64.97 1057
## x 65.60 1112
## x 65.62 1105
## x 65.63 1104
## x 65.65 1134
## x 65.67 1117
## x 65.82 1170
## x 65.83 1171
## x 65.85 1159
## x 65.87 1146
## x 65.88 1153
## x 65.90 1163
## x 66.07 1203
## x 66.08 1213
## x 66.10 1205
## x 66.12 1220
## x 66.13 1232
## x 66.15 1236
## x 66.30 1263
## x 66.32 1255
## x 66.33 1252
## x 66.35 1253
## x 66.37 1254
## x 67.90 2140
## x 67.92 2141
## x 68.93 1340
## x 68.95 1342
## x 68.97 1339
## x 68.98 1332
## x 69.08 41
## x 69.08 1388
## x 69.10 42
## x 69.10 1375
## x 69.12 46
## x 69.12 1371
## x 69.13 8
## x 69.13 1380
## x 69.15 9
## x 69.15 1365
## x 69.17 10
## x 69.27 1415
## x 69.28 1407
## x 69.30 1412
## x 69.32 1413
## x 69.43 1460
## x 69.45 1482
## x 69.47 1480
## x 69.48 1479
## x 69.50 1464
## x 69.58 50
## x 69.60 54
## x 69.62 64
## x 69.63 65
## x 69.65 66
## x 69.65 2209
## x 69.67 2210
## x 69.68 2221
## x 69.70 2231
## x 69.82 1502
## x 69.83 1498
## x 69.85 106
## x 69.85 1499
## x 69.87 107
## x 69.87 1506
## x 69.88 108
## x 69.88 1522
## x 69.90 109
## x 69.92 101
## x 69.93 98
## x 69.95 92
## x 70.02 1559
## x 70.03 1572
## x 70.05 1573
## x 70.07 1577
## x 70.08 1563
## x 70.12 166
## x 70.13 154
## x 70.13 2282
## x 70.15 151
## x 70.15 2280
## x 70.17 163
## x 70.17 2268
## x 70.18 178
## x 70.18 2294
## x 70.20 174
## x 70.20 1607
## x 70.22 1616
## x 70.23 1621
## x 70.25 1622
## x 70.27 1596
## x 70.37 215
## x 70.38 229
## x 70.40 226
## x 70.42 218
## x 70.43 199
## x 70.45 223
## x 70.45 1636
## x 70.47 1637
## x 70.48 1681
## x 70.50 1682
## x 70.52 1677
## x 70.63 274
## x 70.65 280
## x 70.67 276
## x 70.67 1722
## x 70.68 254
## x 70.68 1723
## x 70.70 278
## x 70.70 1720
## x 70.72 282
## x 70.72 1721
## x 70.73 1710
## x 70.75 1702
## x 70.88 1760
## x 70.90 1761
## x 70.92 1766
## x 70.93 1758
## x 70.95 323
## x 70.95 1764
## x 70.97 311
## x 70.98 299
## x 71.00 300
## x 71.02 316
## x 71.03 330
## x 71.05 2325
## x 71.05 2339
## x 71.07 2313
## x 71.08 1786
## x 71.08 2315
## x 71.10 1826
## x 71.10 2312
## x 71.12 1823
## x 71.12 2314
## x 71.13 1816
## x 71.15 1808
## x 71.23 2348
## x 71.25 2345
## x 71.27 2363
## x 71.28 2364
## x 71.30 1867
## x 71.32 1868
## x 71.33 1864
## x 71.35 1841
## x 71.50 366
## x 71.52 357
## x 71.53 354
## x 71.55 343
## x 71.70 1892
## x 71.72 400
## x 71.72 1905
## x 71.73 388
## x 71.73 1922
## x 71.75 391
## x 71.75 1918
## x 71.77 396
## x 71.77 1904
## x 71.78 389
## x 71.80 399
## x 71.92 1961
## x 71.93 1966
## x 71.95 1963
## x 71.97 441
## x 71.97 1959
## x 71.98 451
## x 71.98 1945
## x 72.00 448
## x 72.02 472
## x 72.03 477
## x 72.12 1986
## x 72.13 1983
## x 72.15 1988
## x 72.17 2022
## x 72.18 2010
## x 72.22 514
## x 72.23 502
## x 72.25 503
## x 72.27 490
## x 72.28 492
## x 72.30 507
## x 72.38 2065
## x 72.40 2069
## x 72.42 2063
## x 72.43 2059
## x 72.45 2034
## x 72.48 547
## x 72.50 561
## x 72.52 554
## x 72.53 569
## x 72.55 557
## x 72.58 2110
## x 72.60 2111
## x 72.62 2112
## x 72.63 2109
## x 72.65 2114
## x 72.82 614
## x 72.83 584
## x 72.85 589
## x 72.87 599
## x 72.88 619
## x 72.90 616
## x 72.92 621
## x 73.08 647
## x 73.10 670
## x 73.12 667
## x 73.13 675
## x 73.15 678
## x 73.17 673
## x 73.33 729
## x 73.35 728
## x 73.37 716
## x 73.38 717
## x 73.40 705
## x 73.42 719
## x 73.57 754
## x 73.58 760
## x 73.60 761
## x 73.62 766
## x 73.63 781
## x 73.87 818
## x 73.88 819
## x 73.90 820
## x 73.92 821
## x 73.93 822
## x 75.88 859
## x 75.90 860
## x 75.92 873
## x 75.93 874
## x 75.95 872
## x 76.52 909
## x 76.53 910
## x 76.55 911
## x 76.57 926
## x 76.58 923
## x 77.33 981
## x 77.35 982
## x 77.37 983
## x 77.38 980
## x 77.40 994
## x 77.53 1046
## x 77.55 1050
## x 77.57 1038
## x 77.58 1043
## x 77.60 1036
## x 77.95 1087
## x 77.97 1065
## x 77.98 1097
## x 78.00 1085
## x 78.02 1093
## x 78.73 1136
## x 78.75 1124
## x 78.77 1125
## x 78.78 1126
## x 78.80 1127
## x 78.97 1168
## x 78.98 1182
## x 79.00 1183
## x 79.02 1184
## x 79.03 1185
## x 79.17 1204
## x 79.18 1201
## x 79.20 1215
## x 79.22 1216
## x 79.23 1235
## x 79.35 1270
## x 79.37 1285
## x 79.38 1295
## x 79.40 1293
## x 79.42 1289
## x 82.82 2161
## x 82.83 2152
## x 82.85 2139
## x 82.87 2138
## x 83.70 1313
## x 83.72 1314
## x 83.73 1319
## x 83.75 1336
## x 83.77 1337
## x 83.83 2187
## x 83.85 2188
## x 83.87 2198
## x 83.88 1376
## x 83.88 2179
## x 83.90 1377
## x 83.90 2190
## x 83.92 1373
## x 83.93 1369
## x 83.95 1378
## x 84.07 1409
## x 84.08 1406
## x 84.10 1397
## x 84.12 1398
## x 84.13 1399
## x 84.25 21
## x 84.25 1438
## x 84.27 22
## x 84.27 1442
## x 84.28 23
## x 84.28 1449
## x 84.30 24
## x 84.30 1468
## x 84.32 1469
## x 84.55 77
## x 84.57 79
## x 84.58 80
## x 84.60 81
## x 84.62 1496
## x 84.63 1492
## x 84.65 1493
## x 84.65 2212
## x 84.67 1529
## x 84.67 2218
## x 84.68 1523
## x 84.68 2211
## x 84.70 2246
## x 84.72 120
## x 84.73 125
## x 84.75 131
## x 84.77 137
## x 84.80 1561
## x 84.82 1566
## x 84.83 1567
## x 84.85 1564
## x 84.87 159
## x 84.87 1549
## x 84.88 164
## x 84.90 165
## x 84.92 171
## x 84.98 1605
## x 85.00 1606
## x 85.02 224
## x 85.02 1600
## x 85.03 230
## x 85.03 1608
## x 85.05 235
## x 85.05 1601
## x 85.07 222
## x 85.12 2284
## x 85.13 2285
## x 85.15 2273
## x 85.17 2295
## x 85.18 1634
## x 85.20 267
## x 85.20 1635
## x 85.22 268
## x 85.22 1680
## x 85.23 269
## x 85.23 1678
## x 85.25 270
## x 85.25 1673
## x 85.38 324
## x 85.38 1704
## x 85.40 298
## x 85.40 1709
## x 85.42 289
## x 85.42 1714
## x 85.43 292
## x 85.43 1715
## x 85.45 314
## x 85.45 1707
## x 85.60 1745
## x 85.62 1750
## x 85.63 1747
## x 85.65 1748
## x 85.67 1755
## x 85.73 360
## x 85.75 342
## x 85.77 351
## x 85.78 379
## x 85.80 367
## x 85.80 1803
## x 85.82 368
## x 85.82 1804
## x 85.83 1821
## x 85.85 1817
## x 85.87 1813
## x 85.95 417
## x 85.97 390
## x 85.98 397
## x 86.00 398
## x 86.02 414
## x 86.02 1856
## x 86.03 424
## x 86.03 1853
## x 86.05 1850
## x 86.07 1846
## x 86.08 1837
## x 86.13 460
## x 86.15 461
## x 86.17 458
## x 86.18 478
## x 86.20 479
## x 86.32 515
## x 86.33 516
## x 86.35 504
## x 86.37 518
## x 86.38 526
## x 86.42 1914
## x 86.43 1915
## x 86.45 1916
## x 86.47 1912
## x 86.48 574
## x 86.48 1895
## x 86.50 576
## x 86.52 568
## x 86.53 564
## x 86.55 570
## x 86.60 1951
## x 86.62 1952
## x 86.63 1953
## x 86.65 1949
## x 86.65 2306
## x 86.67 1936
## x 86.67 2307
## x 86.68 601
## x 86.68 627
## x 86.68 2318
## x 86.70 602
## x 86.70 2332
## x 86.72 612
## x 86.73 617
## x 86.80 1980
## x 86.80 2350
## x 86.82 1993
## x 86.82 2355
## x 86.83 663
## x 86.83 1994
## x 86.83 2352
## x 86.85 639
## x 86.85 2016
## x 86.85 2349
## x 86.87 641
## x 86.87 2014
## x 86.88 638
## x 86.90 633
## x 86.98 2051
## x 87.00 682
## x 87.00 2052
## x 87.02 681
## x 87.02 2057
## x 87.03 710
## x 87.03 2053
## x 87.05 724
## x 87.05 2039
## x 87.07 697
## x 87.17 2096
## x 87.18 711
## x 87.18 2089
## x 87.20 712
## x 87.20 2098
## x 87.22 713
## x 87.22 2099
## x 87.23 714
## x 87.23 2100
## x 87.25 702
## x 87.25 2093
## x 87.37 790
## x 87.38 799
## x 87.40 783
## x 87.42 787
## x 87.43 801
## x 87.45 785
## x 91.17 849
## x 91.18 850
## x 91.20 851
## x 91.70 891
## x 91.72 889
## x 91.73 890
## x 92.17 944
## x 92.18 945
## x 92.20 946
## x 92.32 1015
## x 92.33 1027
## x 92.35 1045
## x 92.37 1032
## x 92.38 1037
## x 92.82 1071
## x 92.83 1072
## x 92.85 1064
## x 92.87 1055
## x 93.32 1130
## x 93.33 1118
## x 93.35 1119
## x 93.37 1102
## x 93.62 1176
## x 93.63 1164
## x 93.65 1192
## x 93.67 1181
## x 93.80 1217
## x 93.82 1218
## x 93.83 1229
## x 94.00 1291
## x 94.02 1265
## x 94.03 1266
## x 94.05 1294
## x 97.12 2135
## x 97.13 2136
## x 97.15 2137
## x 97.67 2201
## x 97.68 2197
## x 97.70 2207
## x 97.72 2204
## x 97.73 2199
## x 97.75 2191
## x 97.77 2178
## x 97.78 2170
## x 98.72 1321
## x 98.73 1322
## x 98.75 1327
## x 98.77 1323
## x 98.78 1301
## x 98.90 1382
## x 98.90 2249
## x 98.92 1367
## x 98.92 2250
## x 98.93 1360
## x 98.95 1345
## x 99.10 1402
## x 99.12 1403
## x 99.12 2243
## x 99.13 1393
## x 99.13 2219
## x 99.15 1432
## x 99.15 2220
## x 99.17 1435
## x 99.17 2254
## x 99.28 1477
## x 99.30 1473
## x 99.32 1478
## x 99.33 1467
## x 99.57 40
## x 99.58 47
## x 99.60 44
## x 99.62 28
## x 99.63 33
## x 99.63 1501
## x 99.65 1497
## x 99.67 1526
## x 99.67 2269
## x 99.68 1530
## x 99.68 2261
## x 99.70 2258
## x 99.72 2263
## x 99.82 1565
## x 99.83 1570
## x 99.85 1571
## x 99.87 1568
## x 99.88 1569
## x 99.93 87
## x 99.95 84
## x 99.97 76
## x 99.98 61
## x 100.03 1629
## x 100.05 1610
## x 100.07 1604
## x 100.08 117
## x 100.08 1612
## x 100.10 118
## x 100.10 1617
## x 100.12 128
## x 100.13 95
## x 100.15 96
## x 100.22 1646
## x 100.23 1639
## x 100.25 161
## x 100.25 1632
## x 100.27 153
## x 100.27 1649
## x 100.28 144
## x 100.28 1650
## x 100.30 138
## x 100.32 140
## x 100.33 149
## x 100.42 1728
## x 100.43 1713
## x 100.45 191
## x 100.45 1718
## x 100.47 202
## x 100.47 1719
## x 100.48 196
## x 100.48 1716
## x 100.50 193
## x 100.52 194
## x 100.62 1762
## x 100.63 1754
## x 100.65 1751
## x 100.67 1752
## x 100.68 1757
## x 100.70 213
## x 100.70 2337
## x 100.72 211
## x 100.72 2334
## x 100.73 214
## x 100.73 2326
## x 100.75 205
## x 100.77 206
## x 100.78 203
## x 100.80 208
## x 100.82 212
## x 100.83 1783
## x 100.85 1819
## x 100.85 2344
## x 100.87 1824
## x 100.87 2354
## x 100.88 1822
## x 100.88 2357
## x 100.90 1818
## x 100.90 2358
## x 100.97 275
## x 100.98 272
## x 101.00 264
## x 101.02 265
## x 101.03 279
## x 101.03 1860
## x 101.05 1857
## x 101.07 1854
## x 101.08 1863
## x 101.10 1859
## x 101.15 335
## x 101.17 337
## x 101.18 325
## x 101.20 326
## x 101.22 336
## x 101.50 1907
## x 101.52 1919
## x 101.53 375
## x 101.53 1920
## x 101.55 377
## x 101.55 1921
## x 101.57 378
## x 101.57 1917
## x 101.58 384
## x 101.60 361
## x 101.70 1965
## x 101.72 418
## x 101.72 1956
## x 101.73 406
## x 101.73 1957
## x 101.75 407
## x 101.75 1962
## x 101.77 408
## x 101.77 1954
## x 101.78 386
## x 101.88 1990
## x 101.90 440
## x 101.90 1978
## x 101.92 445
## x 101.92 2000
## x 101.93 442
## x 101.93 1991
## x 101.95 438
## x 101.95 2002
## x 101.97 444
## x 101.98 439
## x 102.07 2062
## x 102.08 2056
## x 102.10 2061
## x 102.12 498
## x 102.12 2046
## x 102.13 512
## x 102.13 2043
## x 102.15 523
## x 102.17 500
## x 102.18 501
## x 102.20 489
## x 102.27 2118
## x 102.28 2097
## x 102.30 2086
## x 102.32 548
## x 102.32 2083
## x 102.33 536
## x 102.33 2088
## x 102.35 566
## x 102.37 558
## x 102.38 559
## x 102.52 603
## x 102.53 623
## x 102.55 620
## x 102.57 628
## x 102.58 626
## x 102.70 662
## x 102.72 668
## x 102.73 660
## x 102.75 676
## x 102.77 655
## x 102.88 722
## x 102.90 736
## x 102.92 734
## x 102.93 735
## x 102.95 739
## x 102.97 737
## x 103.10 782
## x 103.12 777
## x 103.13 780
## x 103.15 775
## x 103.17 762
## x 103.28 811
## x 103.30 812
## x 103.32 813
## x 103.33 814
## x 103.35 815
## x 103.37 816
## x 106.13 841
## x 106.15 842
## x 106.17 843
## x 106.18 844
## x 106.20 845
## x 106.73 886
## x 106.75 883
## x 106.77 882
## x 106.78 884
## x 106.80 888
## x 107.35 990
## x 107.37 991
## x 107.38 988
## x 107.40 965
## x 107.42 964
## x 107.70 1007
## x 107.72 1012
## x 107.73 1013
## x 107.75 1042
## x 107.77 1034
## x 107.78 1011
## x 108.23 1091
## x 108.25 1079
## x 108.27 1099
## x 108.28 1094
## x 108.30 1092
## x 108.93 1128
## x 108.95 1139
## x 108.97 1131
## x 108.98 1132
## x 109.00 1110
## x 109.15 1149
## x 109.17 1175
## x 109.18 1187
## x 109.20 1178
## x 109.22 1179
## x 109.37 1224
## x 109.38 1225
## x 109.40 1208
## x 109.42 1209
## x 109.43 1210
## x 109.60 1250
## x 109.62 1281
## x 109.63 1292
## x 109.65 1284
## x 109.67 1271
## x 112.53 2143
## x 112.55 2144
## x 112.57 2145
## x 113.10 2165
## x 113.12 2171
## x 113.13 2172
## x 113.15 2168
## x 113.70 1307
## x 113.72 1304
## x 113.73 1305
## x 113.75 1306
## x 113.77 1311
## x 113.88 1381
## x 113.88 2239
## x 113.90 1363
## x 113.90 2252
## x 113.92 1374
## x 113.92 2227
## x 113.93 1359
## x 113.93 2228
## x 113.95 1353
## x 114.07 1404
## x 114.07 1414
## x 114.08 1395
## x 114.10 1392
## x 114.12 27
## x 114.12 1394
## x 114.13 16
## x 114.15 17
## x 114.17 18
## x 114.23 1436
## x 114.25 1451
## x 114.27 1470
## x 114.28 1452
## x 114.30 1448
## x 114.35 2275
## x 114.37 2289
## x 114.38 2286
## x 114.40 2290
## x 114.43 69
## x 114.45 48
## x 114.47 49
## x 114.48 51
## x 114.60 123
## x 114.60 1505
## x 114.62 115
## x 114.62 1500
## x 114.63 135
## x 114.63 1487
## x 114.65 121
## x 114.65 1485
## x 114.67 1488
## x 114.77 180
## x 114.78 167
## x 114.78 1547
## x 114.80 142
## x 114.80 1558
## x 114.82 152
## x 114.82 1552
## x 114.83 1537
## x 114.85 1538
## x 114.92 231
## x 114.93 219
## x 114.95 220
## x 114.97 225
## x 114.98 232
## x 115.00 1615
## x 115.02 1613
## x 115.03 1597
## x 115.05 1590
## x 115.07 1587
## x 115.10 2298
## x 115.12 277
## x 115.12 284
## x 115.12 2300
## x 115.13 285
## x 115.13 2308
## x 115.15 287
## x 115.15 2305
## x 115.17 286
## x 115.22 1640
## x 115.23 1643
## x 115.25 1658
## x 115.25 2361
## x 115.27 329
## x 115.27 1659
## x 115.27 2362
## x 115.28 321
## x 115.28 1660
## x 115.28 2376
## x 115.30 331
## x 115.32 332
## x 115.33 310
## x 115.40 1717
## x 115.42 1712
## x 115.43 1703
## x 115.45 1684
## x 115.47 1689
## x 115.58 1763
## x 115.60 1759
## x 115.62 1740
## x 115.63 1734
## x 115.65 1738
## x 115.72 338
## x 115.73 349
## x 115.75 359
## x 115.77 346
## x 115.78 353
## x 115.78 1796
## x 115.80 1797
## x 115.82 1814
## x 115.83 1795
## x 115.85 1800
## x 115.90 402
## x 115.92 403
## x 115.93 419
## x 115.95 420
## x 115.97 394
## x 115.98 1861
## x 116.00 1862
## x 116.02 1851
## x 116.03 1836
## x 116.05 1830
## x 116.12 452
## x 116.12 456
## x 116.13 467
## x 116.15 468
## x 116.17 459
## x 116.30 497
## x 116.32 511
## x 116.33 525
## x 116.35 517
## x 116.37 527
## x 116.47 1890
## x 116.48 1881
## x 116.50 545
## x 116.50 1894
## x 116.52 546
## x 116.52 1887
## x 116.53 573
## x 116.53 1888
## x 116.55 572
## x 116.57 563
## x 116.67 1960
## x 116.68 611
## x 116.68 1942
## x 116.70 608
## x 116.70 1931
## x 116.72 618
## x 116.72 1928
## x 116.73 624
## x 116.73 1933
## x 116.75 610
## x 116.87 659
## x 116.87 1981
## x 116.88 669
## x 116.88 1975
## x 116.90 661
## x 116.90 2001
## x 116.92 649
## x 116.92 2003
## x 116.93 640
## x 116.93 2008
## x 117.05 703
## x 117.07 704
## x 117.08 692
## x 117.08 2055
## x 117.10 706
## x 117.10 2040
## x 117.12 683
## x 117.12 2027
## x 117.13 2032
## x 117.15 2037
## x 117.23 746
## x 117.25 744
## x 117.27 763
## x 117.28 776
## x 117.30 772
## x 117.30 2104
## x 117.32 2077
## x 117.33 2076
## x 117.35 2071
## x 117.37 2078
## x 117.42 798
## x 117.43 795
## x 117.45 796
## x 117.47 797
## x 117.48 789
## x 117.50 784
## x 120.33 848
## x 120.35 861
## x 120.37 862
## x 120.38 863
## x 120.40 864
## x 121.07 914
## x 121.08 915
## x 121.10 916
## x 121.12 925
## x 121.13 919
## x 122.00 985
## x 122.02 986
## x 122.03 987
## x 122.05 951
## x 122.07 952
## x 122.22 1031
## x 122.47 1033
## x 122.48 1035
## x 122.50 1024
## x 122.52 1028
## x 122.53 1005
## x 122.98 1081
## x 123.00 1090
## x 123.02 1082
## x 123.03 1068
## x 123.05 1060
## x 124.35 1115
## x 124.37 1121
## x 124.38 1122
## x 124.40 1109
## x 124.42 1123
## x 124.58 1169
## x 124.60 1166
## x 124.62 1180
## x 124.63 1158
## x 124.65 1155
## x 124.83 1223
## x 124.85 1221
## x 124.87 1199
## x 124.88 1195
## x 125.00 1267
## x 125.02 1268
## x 125.03 1286
## x 125.05 1287
## x 125.07 1288
## x 128.72 1334
## x 128.73 1335
## x 128.75 1343
## x 128.77 1333
## x 128.83 2134
## x 128.85 2162
## x 128.87 1379
## x 128.87 2159
## x 128.88 1386
## x 128.90 1383
## x 128.92 1384
## x 129.03 1419
## x 129.05 1424
## x 129.07 1416
## x 129.08 1417
## x 129.17 39
## x 129.18 45
## x 129.20 37
## x 129.22 43
## x 129.22 1439
## x 129.23 1437
## x 129.25 1458
## x 129.27 1463
## x 129.28 1472
## x 129.47 2206
## x 129.48 2202
## x 129.50 82
## x 129.50 2200
## x 129.52 70
## x 129.53 57
## x 129.55 58
## x 129.58 1513
## x 129.60 1510
## x 129.62 1511
## x 129.63 1503
## x 129.65 99
## x 129.65 1508
## x 129.67 133
## x 129.68 130
## x 129.70 134
## x 129.77 1531
## x 129.78 1574
## x 129.80 1575
## x 129.82 1576
## x 129.83 170
## x 129.83 1579
## x 129.85 172
## x 129.87 173
## x 129.88 183
## x 129.95 1623
## x 129.97 1624
## x 129.98 1620
## x 130.00 217
## x 130.00 1625
## x 130.02 227
## x 130.02 1626
## x 130.03 228
## x 130.05 234
## x 130.13 1648
## x 130.15 256
## x 130.15 1645
## x 130.17 244
## x 130.17 1641
## x 130.18 249
## x 130.18 1642
## x 130.18 2226
## x 130.20 246
## x 130.20 1647
## x 130.20 2214
## x 130.22 260
## x 130.22 1644
## x 130.22 2215
## x 130.23 2216
## x 130.32 288
## x 130.33 296
## x 130.33 1726
## x 130.35 317
## x 130.35 1727
## x 130.37 291
## x 130.37 1724
## x 130.38 304
## x 130.38 1725
## x 130.40 1730
## x 130.55 1772
## x 130.57 1768
## x 130.58 1765
## x 130.60 1770
## x 130.62 1767
## x 130.62 2293
## x 130.63 2277
## x 130.65 2278
## x 130.70 355
## x 130.72 340
## x 130.73 345
## x 130.73 1784
## x 130.75 339
## x 130.75 1782
## x 130.77 344
## x 130.77 1802
## x 130.78 1788
## x 130.80 1827
## x 130.90 415
## x 130.92 423
## x 130.92 1866
## x 130.93 431
## x 130.93 1871
## x 130.95 432
## x 130.95 1872
## x 130.97 433
## x 130.97 1873
## x 130.98 1865
## x 131.08 462
## x 131.10 463
## x 131.12 464
## x 131.13 450
## x 131.15 455
## x 131.27 520
## x 131.28 521
## x 131.30 522
## x 131.32 482
## x 131.33 495
## x 131.38 1924
## x 131.40 1925
## x 131.42 1926
## x 131.43 551
## x 131.43 1923
## x 131.45 565
## x 131.45 1909
## x 131.47 567
## x 131.48 533
## x 131.48 2341
## x 131.50 539
## x 131.50 2323
## x 131.52 2310
## x 131.53 2324
## x 131.57 1972
## x 131.58 1967
## x 131.60 1971
## x 131.62 1964
## x 131.62 2380
## x 131.63 607
## x 131.63 1946
## x 131.63 2381
## x 131.65 604
## x 131.65 2382
## x 131.67 591
## x 131.68 585
## x 131.70 586
## x 131.75 1987
## x 131.77 1992
## x 131.78 2021
## x 131.80 2019
## x 131.82 637
## x 131.82 2007
## x 131.83 643
## x 131.85 653
## x 131.87 634
## x 131.88 644
## x 131.95 2070
## x 131.97 2067
## x 131.98 2068
## x 132.00 2064
## x 132.02 687
## x 132.02 2031
## x 132.03 684
## x 132.05 699
## x 132.07 700
## x 132.08 691
## x 132.15 2119
## x 132.17 2120
## x 132.18 2121
## x 132.20 748
## x 132.20 2122
## x 132.22 741
## x 132.22 2123
## x 132.23 774
## x 132.23 2124
## x 132.25 751
## x 132.27 740
## x 132.38 831
## x 132.40 832
## x 132.42 833
## x 132.43 834
## x 132.45 835
## x 132.47 836
## x 137.02 852
## x 137.03 853
## x 137.05 854
## x 137.65 887
## x 137.67 895
## x 137.68 930
## x 137.70 929
## x 138.15 943
## x 138.17 935
## x 138.18 936
## x 138.20 933
## x 138.22 934
## x 138.30 1039
## x 138.32 1029
## x 138.35 1020
## x 138.37 1048
## x 138.38 1021
## x 138.40 1025
## x 138.87 1056
## x 138.88 1074
## x 138.90 1075
## x 138.92 1076
## x 138.93 1086
## x 139.65 1191
## x 139.67 1193
## x 139.68 1161
## x 139.70 1167
## x 140.20 1230
## x 140.22 1231
## x 140.23 1228
## x 140.25 1211
## x 140.43 1278
## x 140.45 1279
## x 140.47 1280
## x 140.48 1296
##
##
##
## Table: Differences detected by variable
##
## var.x var.y n NAs
## -------------------------- -------------------------- --- ----
## KrillID KrillID 0 0
## SensorName SensorName 0 0
## Date Date 0 0
## Time Time 0 0
## User User 0 0
## SensorID SensorID 0 0
## Unit Unit 0 0
## Value Value 0 0
## Unit.1 Unit.1 0 0
## Mode Mode 0 0
## Phase Phase 0 0
## Unit.2 Unit.2 0 0
## Amplitude Amplitude 0 0
## Unit.3 Unit.3 0 0
## Temp Temp 0 0
## Unit.4 Unit.4 0 0
## patm patm 0 0
## Unit.5 Unit.5 0 0
## Salinity Salinity 0 0
## Unit.6 Unit.6 0 0
## Error Error 0 0
## Cal0 Cal0 0 0
## Unit.7 Unit.7 0 0
## T0 T0 0 0
## Unit.8 Unit.8 0 0
## O2.2nd O2.2nd 0 0
## Unit.9 Unit.9 0 0
## Cal2nd Cal2nd 0 0
## Unit.10 Unit.10 0 0
## T2nd T2nd 0 0
## Unit.11 Unit.11 0 0
## CalPressure CalPressure 0 0
## Unit.12 Unit.12 0 0
## f1 f1 0 0
## dPhi1 dPhi1 0 0
## dkSv1 dkSv1 0 0
## dPhi2 dPhi2 0 0
## dkSv2 dkSv2 0 0
## m m 0 0
## Cal.Mode Cal.Mode 0 0
## SignalLEDCurrent SignalLEDCurrent 0 0
## ReferenceLEDCurrent ReferenceLEDCurrent 0 0
## ReferenceAmplitude ReferenceAmplitude 0 0
## DeviceSerial DeviceSerial 0 0
## FwVersion FwVersion 0 0
## SensorType SensorType 0 0
## BatchID BatchID 0 0
## Calibration.Date Calibration.Date 0 0
## Sensor.lot Sensor.lot 0 0
## PreSens.calibrated.Sensor PreSens.calibrated.Sensor 0 0
## Battery.Voltage Battery.Voltage 0 0
## Unit.13 Unit.13 0 0
## Sequence Sequence 0 0
## FileName FileName 0 0
## MOATS MOATS 0 0
## Treatment Treatment 0 0
## LoadingLocation LoadingLocation 0 0
## GlassVialNumber GlassVialNumber 0 0
## Species Species 0 0
## TelsonLength..mm. TelsonLength..mm. 0 0
## Size Size 0 0
## dateTime dateTime 0 0
## TrialID.x TrialID.x 0 0
## percentDOassumpt percentDOassumpt 0 0
## assumedSatDOmg assumedSatDOmg 0 0
## percentDO percentDO 0 0
## obseveredSatDOmg obseveredSatDOmg 0 0
## actualDOmg actualDOmg 0 0
## CorTemp CorTemp 0 0
## SalinityConstant SalinityConstant 0 0
## oxygen oxygen 0 0
## sapply(cSlopes, I) sapply(cSlopes, I) 0 0
## TrialID.y TrialID.y 0 0
##
##
##
## Table: Differences detected
##
## | |
## |:-----------------------|
## |No differences detected |
##
##
##
## Table: Non-identical attributes
##
## | |
## |:---------------------------|
## |No non-identical attributes |9.C Comparison between Krillr4_58 and 59 to better determine differences
Using neighboring observations to determine if any difference
dRESP_58 <- read.csv(file = "2020.11.16_REPRwork_KRLr4_58comparison.csv", stringsAsFactors = FALSE)
dRESP_59 <- read.csv(file = "2020.11.16_REPRwork_KRLr4_59comparison.csv", stringsAsFactors = FALSE)
comparedf(dRESP_58, dRESP_59)## Compare Object
##
## Function Call:
## comparedf(x = dRESP_58, y = dRESP_59)
##
## Shared: 52 non-by variables and 41 observations.
## Not shared: 0 variables and 9 observations.
##
## Differences found in 10/52 variables compared.
## 0 variables compared have non-identical attributes.# Compare Object
#
# Function Call:
# comparedf(x = dRESP_58, y = dRESP_59)
#
# Shared: 52 non-by variables and 41 observations.
# Not shared: 0 variables and 9 observations.
#
# Differences found in 10/52 variables compared.
# 0 variables compared have non-identical attributes.
comparedf(dRESP_58, dRESP_59, by = "delta_t")## Compare Object
##
## Function Call:
## comparedf(x = dRESP_58, y = dRESP_59, by = "delta_t")
##
## Shared: 51 non-by variables and 0 observations.
## Not shared: 0 variables and 91 observations.
##
## Differences found in 0/51 variables compared.
## 0 variables compared have non-identical attributes.9.d Comparing the slopes of 58 and 59
write.csv(GRP_dtotal, file = "2020.11.16_GRP_dtotalObservations.csv", row.names = FALSE)
GRP_dtotal_58 <- read.csv(file = "2020.11.16_GRP_dtotalObservationsKRL4_58.csv", stringsAsFactors = FALSE)
dtotal_58_filter <- filter(dtotal, KrillID == "Trial04_KRLr4_58")
GRP_dtotal_59 <- read.csv(file = "2020.11.16_GRP_dtotalObservationsKRL4_59.csv", stringsAsFactors = FALSE)
dtotal_59_filter <- filter(dtotal, KrillID == "Trial04_KRLr4_59")
GRP_dtotal_68 <- read.csv(file = "2020.11.16_GRP_dtotalObservationsKRL4_68.csv", stringsAsFactors = FALSE)
GRP_dtotal_69 <- read.csv(file = "2020.11.16_GRP_dtotalObservationsKRL4_69.csv", stringsAsFactors = FALSE)
# Krill58plot <- ggplot(GRP_dtotal_58,
# aes(x=Time,
# y=oxygen))
#
# Krill58plot
Krill58plot2 <- ggplot(GRP_dtotal_58, aes(delta_t, oxygen)) +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = GRP_dtotal_58, aes(x=delta_t, y=oxygen), size=5, color = "purple") +
ggtitle("Boxplot for Krill 58 observations") +
theme_bw()
Krill58plot2## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
9.x T/S the linear model list function
# levels(dtotal$KrillID)
#
# ##### Krill 58
# lm58 <- lm(oxygen ~ delta_t, GRP_dtotal_58, na.action=na.omit)
# info58 <- lmList(oxygen ~ delta_t|KrillID, GRP_dtotal_58, na.action=na.omit)
# GRP_dtotal_58$KrillID <- factor(GRP_dtotal_58$KrillID)
# levels(GRP_dtotal_58$KrillID)
#
#
# lm58f <- lm(oxygen ~ delta_t, dtotal_58_filter, na.action=na.omit)
# info58f <- lmList(oxygen ~ delta_t|KrillID, dtotal_58_filter, na.action=na.omit)
#
#
# info58 <- lmList(oxygen ~ delta_t|KrillID, GRP_dtotal_58, na.action=na.omit)
# GRP_dtotal_58$KrillID <- factor(GRP_dtotal_58$KrillID)
# levels(GRP_dtotal_58$KrillID)
#
#
# ### Krill 59
#
# #using the filtered dtotal database rather than kate's copy and paste file
# lm59f <- lm(oxygen ~ delta_t, dtotal_59_filter, na.action=na.omit)
# info59f <- lmList(oxygen ~ delta_t|KrillID, dtotal_59_filter, na.action=na.omit)
# levels(dtotal_59_filter$KrillID)
#
#
# # working off of kate's copy paste file
# lm59 <- lm(oxygen ~ delta_t, GRP_dtotal_59, na.action=na.omit)
# info59 <- lmList(oxygen ~ delta_t|KrillID, GRP_dtotal_59, na.action=na.omit)
#
#
# #GRP_dtotal_59 <- subset(GRP_dtotal_59, KrillID != "")
# GRP_dtotal_59a <- filter(GRP_dtotal_59, KrillID = "")
#
#
# GRP_dtotal_59$KrillID <- factor(GRP_dtotal_59$KrillID)
# levels(GRP_dtotal_59$KrillID)
#
#
#
# ### hunting for missing values
# dtotal59fmissingOXY <- filter(dtotal_59_filter, is.na(oxygen))
# dotal59fmissingDELT <- filter(dtotal_59_filter, is.na(delta_t))
# # no missing values found
write.csv(dtotal, file = "2020.11.16_dtotal_TSlinearmodel.csv", row.names = FALSE)9.e Creating a small box plot for Krill 59
Krill59plot <- ggplot(GRP_dtotal_59, aes(delta_t, oxygen)) +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = GRP_dtotal_59, aes(x=delta_t, y=oxygen), size=5, color = "purple") +
ggtitle("Boxplot for Krill 59 observations") +
theme_bw()
Krill59plot## Warning: Continuous x aesthetic -- did you forget aes(group=...)?## Warning: Removed 727 rows containing missing values (stat_boxplot).## Warning: Removed 727 rows containing missing values (geom_point).
9.f Creating a small box plot for Krill 68
Krill68plot <- ggplot(GRP_dtotal_68, aes(delta_t, oxygen)) +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = GRP_dtotal_68, aes(x=delta_t, y=oxygen), size=5, color = "purple") +
ggtitle("Boxplot for Krill 58 observations") +
theme_bw()
Krill68plot## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
9.g Creating a small box plot for Krill 69
Krill69plot <- ggplot(GRP_dtotal_69, aes(delta_t, oxygen)) +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = GRP_dtotal_69, aes(x=delta_t, y=oxygen), size=5, color = "purple") +
ggtitle("Boxplot for Krill 58 observations") +
theme_bw()
Krill69plot## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
#10.) Creating the linear Model on the Slope (Sapply)
##10.a) Trouble Shooting the Linear Model Problems
# summary(lm(oxygen ~ delta_t, data= dtotal[dtotal$KrillID=="Trial04_KRLr4_59",]))$r.squared
#
# lm(oxygen ~ delta_t, data= dtotal[dtotal$KrillID=="Trial04_KRLr4_68",])
#
# lm(oxygen ~ delta_t, data= dtotal[dtotal$KrillID=="Trial04_KRLr4_69",])
#
#
# dtotal[is.na(dtotal$delta_t),]
# unique(dtotal$KrillID)
#
# names(info)
#
# view(dtotal[!dtotal$KrillID %in% names(info), ])
#
#
# print(info)
#
# # slopes <- coef(info)[2]
# # #print(slopes)
# # dslopes <- data.matrix(slopes)
# # #View(dref)
# # mode(dslopes[,1])
# # dslopes <- as.data.frame(dslopes)
# # dslopes$slope <- dslopes$delta_t
# # dslopes$delta_t <- NULL
# # #View(dslopes)
#
# #now for R^2!!
# Rsq <- sapply(info,function(x) summary(x)$r.squared)
# t(Rsq) #transposes rows and columns
# Rsq <- data.matrix(Rsq)
# #View(Rsq)
# dtotal <- cbind(ds, Rsq)
# View(dtotal)